Traits Use cases.html

Participants

Mark*, Shawn, Steve, Damian, Benno, Flip

Use Case 1: Predicting species occurrence based on point data (e.g., bird counts) and other biotic & abiotic data. Want to estimate population densities (presence/absence; abundance) of
birds (just as an example; others include specimen location
information, road-kill sitings, etc.) based on growing database of
organismal occurrence data (species/location/date).

Assist researchers
in locating and assembling for further analyses, data sets of
covariates (predictor variables) to inform models of occurrence and
abundance. Scope expansion-- if one has certain species, what behavioral/life history characteristics are available for these?

CONCRETE: integrate bird point data with MODIS data with meteorological data. Determine programmatic access to these sources. (Maybe take lat/lon and semantic map that to Obs model to unify lat/lon anywhere appears, e.g. met data and bird point data).

return number of data sets, can be searched by facets: sector, realm, institution, author, grain/resolution (in space-time) --> semantics of these can be elaborated and referenced from ontology

how determine the catalog of measurements that are available, and the provenance of their values-- modeled surface, nearest gage reading, etc. E.g. there are many ways to estimate a temperature at some given lat/lon. Lots of datasets doing it for some given lat/lon. Which should analyst use? How would semantics guide in resolving these?

what is the nearest freshwater source (stream/pond/lake) to lat/lon? what is growth rate of human popu over last XX years at lat/lon? what is land-use pattern at lat/lon? what are areas having XX diversity of birds at YY time? what studies about [nesting, feeding] in bird sp. X have been done in region Y?

Operations/Tasks:

must have an easy way of discovering and acquiring useful abiotic and biotic data "coverages" to associate with point data; express model outputs as some standard set of coverages to inform other analyses

enable flexible querying of a variety of geospatial coverage data
relative (expressed in common projection with access to " catalog" of
well-defined attributes, which reveal capacity to drill-down or roll-up
those measurements, and include details as to their provenance-- owner,
methodology for collection, etc.) to those point data in order to
better understand underlying ecological drivers for those densities;
enable extraction of coverage data as single-value supplements to table
(e.g. landuse=agric; drill-down to soybean farm)

must have flexible ways to define 'co-location'-- interest might be in radius of importance of features for breeding birds-- proximity to food, water, shelter.

requires extracting coverage data values of a variety of abiotic and
biotic data (land use, human census info, meteorological data, with
flexible radii of relevance. E.g. proximity to freshwater-- 1km away
from 3 major bodies of water or 100m away from small stream. Temporal
aspects of importance as well.

Tagging and retrieving data based on epiphenomena-- e.g. northern migrational front/pattern; involves tracking delta/changes-- when are birds in Mississippi River Delta; when are birds reaching Great Lakes; generally indicating trends--** feedback from analysis/models into metadata/data.Like in genomics commu-- include both highly structured tagging and idiosyncratic taging.

Metrics of completion/success

We refine some of above points, and predetermine subset of results. Challenge expected to automate more comprehensive results (larger number of coverages discovered, selected, rescaled, integrated